{"id":2821,"date":"2018-12-01T21:19:52","date_gmt":"2018-12-01T15:49:52","guid":{"rendered":"http:\/\/navveenbalani.dev\/?p=2821"},"modified":"2019-12-18T21:55:05","modified_gmt":"2019-12-18T16:25:05","slug":"connected-car-iot-solution-using-azure-iot-stack","status":"publish","type":"post","link":"https:\/\/navveenbalani.dev\/index.php\/articles\/connected-car-iot-solution-using-azure-iot-stack\/","title":{"rendered":"Connected Car IoT Solution using Azure IoT stack"},"content":{"rendered":"\n

This article is part of IoT Architecture Series – https:\/\/navveenbalani.dev\/index.php\/articles\/internet-of-things-architecture-components-and-stack-view\/<\/a><\/p>\n\n\n\n

In previous article<\/a>, we went through the Azure IoT stack. In this article, we will go over building the connected car solution.<\/p>\n\n\n\n

The solution strategy comprises of using\nthe Azure IoT services we described earlier to build the connected car IoT\napplication.<\/p>\n\n\n\n

In order to receive messages on Azure platform and eventually start processing\nthe same, there are a bunch of activities that needs to be done.  Our solution uses\ntwo approaches to process the incoming\ndata \u2013 real-time and batch analysis. The real-time approach processes the continuous stream\nof data arriving at IoT Hub from devices that includes taking the required\naction at runtime (like raising an alert, sending data back to devices or\ninvoking a third party service for maintenance order), while batch analysis\nincludes storing the data for further analysis and running complex analytics\njobs or using existing Hadoop jobs for data analysis. The batch analysis\nwould also be used for developing and training the machine learning models\niteratively and then using these deployed models at runtime for real-time\nactions.<\/p>\n\n\n\n

The following image shows the Azure Management portal where a set of task needs to be executed.<\/p>\n\n\n\n

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The following are the high-level steps that need to be performed in Azure\nManagement Portal<\/p>\n\n\n\n

  1. Create Resource Group <\/li>
  2. Create an IoT Hub <\/li>
  3. Create Device Identity <\/li>
  4. Provision Hardware devices  <\/li>
  5. Create Storage Service<\/li>
  6. Create Azure Stream Analytics\nJobs  <\/li>
  7. Create Event Hubs<\/li>
  8. Create PowerBI dashboards<\/li>
  9. Create Notification Hubs<\/li>
  10. Create Machine Learning (ML) model<\/li><\/ol>\n\n\n\n

    We had discussed all of the above capabilities in earlier article, except the Resource Group. A Resource Group basically is a container for all resources related to a specific application, which uses the same subscription information and is hosted in the same location. We create one resource group for the connected car, and all the resources would use this resource group. We would not go over the configuration steps in detail, but summarize one execution flow for the connected car use case which uses the above resources. <\/p>\n\n\n\n

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    Real-time\nFlow<\/strong><\/p>\n\n\n\n

    The IoT Hub receives the data from the connected car device over AMQP protocol. Once the data is received, the stream of data is consumed by Azure Stream Analytic jobs. As part of configuring the Azure Stream Analytics job, you specify the input source as IoT Hub and specify the input format (JSON) and encoding (UTF-8).  This would stream all data from IoT Hub to this Azure Stream Analytics job. As part of output configuration, you specify where you want to store the output of the job, for instance, Blob Storage, Event Hubs, PowerBI, etc. The following image shows the list of output options:<\/p>\n\n\n\n

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    <\/p>\n\n\n\n

    The IoT Hub receives the data from the connected car device over AMQP protocol. Once the data is received, the stream of data is consumed by Azure Stream Analytic jobs. As part of configuring the Azure Stream Analytics job, you specify the input source as IoT Hub and specify the input format (JSON) and encoding (UTF-8).  This would stream all data from IoT Hub to this Azure Stream Analytics job. As part of output configuration, you specify where you want to store the output of the job, for instance, Blob Storage, Event Hubs, PowerBI, etc. The following image shows the list of output options:<\/p>\n\n\n\n

    In the Query Tab on the Azure Stream\nAnalytics job, you specify the query (SQL-like\nqueries), which works on the input data and produces the output. The output (in\nJSON format) is delivered to the output\nchannel.<\/p>\n\n\n\n

    For our connected car scenario, we create two Azure Stream Analytics jobs. For the first job, we specify the input as IoT Hub. The query is to select all the incoming data. There are two output configuration specified; one output dumps the data into Azure Blob for further analysis and the other dumps the data into PowerBI for creating a dashboard. The following image shows the snippet of Query view:<\/p>\n\n\n\n

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    For the second Azure Stream Analytics Job,\nwe create two inputs; the first input is\nIoT Hub, and other is the Asset DB, which contains the asset metadata. For the\nquery, we create condition-based rules which trigger if conditions are not met (like speed >100 km\/hour, low\nengine oil, low tire pressure). The Azure Stream Analytics rules correlates\nasset metadata and runtime data of the connected car to trigger conditions\nbased on asset specifications. The asset specification contains the asset\ndetails and ideal permissible limit of the asset \u2013 be it car engine, tire pressure, engine oil, etc. This is simple\ncondition based maintenance.  The result\nfrom the rules is stored in output\nstorage. The output is stored in an Event\nHubs for further processing by various applications.<\/p>\n\n\n\n

    A custom Event Handler is created which acts as a consumer and picks\nup the data from Event Hubs and uses the Notification Hubs APIs to push high\npriority events to mobiles. The handler also sends updates to web dashboards\nand sends the message back to the IoT Hub device queue for that device using\nthe device id. The connected car device receives the notification on the device\ndashboard.<\/p>\n\n\n\n

    Offline\nProcess<\/strong><\/p>\n\n\n\n

    Now, let\u2019s discuss the offline process. The\noffline process is mainly used for batch\nprocessing, analyzing volumes of data, correlating data from multiple sources\nand complex data flows. The other scenario is developing machine learning\nmodels from these various data sets, training,\nand testing iteratively to build models which can predict or classify with\nreasonable accuracy.<\/p>\n\n\n\n

    Building\nMachine Learning Models<\/strong><\/p>\n\n\n\n

    Building machine learning models is an iterative process, and it involves a bunch of tasks as depicted in the diagram below.<\/p>\n\n\n\n

    <\/p>\n\n\n\n

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    For the connected car solution, we would\nbuild two machine learning models \u2013 one for predictive maintenance and other\nfor driver behavior analysis.<\/p>\n\n\n\n

    The following list shows high-level steps to build machine learning\nmodel using Azure ML. The steps listed below are generic and applicable to building any machine learning model.<\/p>\n\n\n\n